Selection Bias in Value of Travel Time Savings

In this paper we investigate the value of travel time savings. Estimation of this is in most cases based on samples using a specific mode. We use a mixed logit model to estimate the VTTS together with an auxiliary probit equation to capture the fact that the sample is non random. The results show that the probit equation in some cases gives extra information that can be used to improve the VTTS estimates from the mixed logit model. Hence this opens a way to investigate the possible selection bias in standard estimations of VTTS

Analyser af GPS‐data fra ”Test en elbil” og TU data

Artiklen undersøger data fra ”Test en elbil”‐projektet. Husstandes kørsel i deres konventionelle bil og den lånte elbil sammenlignes. Derudover inddrages data fra transportvaneundersøgelsen (TU) for at sammenligne kørselsmønstrene med danskernes daglige kørsel. Resultaterne bekræfter en række gængse formodning, f.eks. at elbiler benyttes til kortere ture, samt at motorveje undgås med elbiler. Derudover viser resultaterne, at kun 12,1 % af turkæderne fra TU ikke kan klares med elbiler. Resultatet bestyrker, at elbilerne har et potentiale til dagligdags transport for mange danskere, såfremt der tilbydes passende opladningsmuligheder eller andre transportmuligheder for at dække de få turkæder, som ikke kan klares med elbiler

Bilvalg under påvirkning af en skattereform og stigende brændstofpriser

Artiklen undersøger, hvordan bilpriser, brændstofpriser og udviklingen i bilkarakteristika påvirker individers bilvalg. For at sammenholde effekterne opstilles en diskret valgmodel, som beskriver danskernes bilvalg ved køb af en ny bil i årene 2005-2008. Modellen anvendes til at undersøge effekten af 2007-reformen, som ændrede registreringsafgiften, og sammenholde reformens effekter med tilsvarende effekter fra stigende brændstofpriser og den teknologiske udvikling i bilernes karakteristika. Resultaterne viser, at de ændringer i nybilssalget, som fandt sted fra 2007 til 2008, kan skyldes flere faktorer bl.a. reformen, stigende benzinpriser og den teknologiske udvikling. Desuden viser resultaterne, at den teknologiske udvikling har haft en langt større effekt end reformen på valget af biltype

Harnessing big data for estimating the energy consumption and driving range of electric vehicles

Analyzing the factors that affect the energy efficiency of vehicles is crucial to the overall improvement of the environmental efficiency of the transport sector, one of the top polluting sectors at the global level. This study analyzes the energy consumption rate (ECR) and driving range (DR) of battery electric vehicles (BEVs) and provides insight into the factors that affect their energy consumption by harnessing big data from real-world driving. The analysis relied on four data sources: (i) driving patterns collected from 741 drivers over a two-year period; (ii) drivers’ characteristics; (iii) road type; (iv) weather conditions. The results of the analysis measure the mean ECR of BEVs at 0.183 kWh/km, underline a 34% increase in ECR and a 25% decrease in DR in the winter with respect to the summer, and suggest the electricity tariff for BEVs to be cost efficient with respect to conventional ones. Moreover, the results of the analysis show that driving speed, acceleration and temperature have non-linear effects on the ECR, while season and precipitation level have a strong linear effect. The econometric model of the ECR of BEVs suggests that the optimal driving speed is between 45 and 56 km/h and the ideal temperature from an energy efficiency perspective is 14 °C. Clearly, the performance of BEVs highly depends on the driving environment, the driving patterns, and the weather conditions, and the findings from this study enlighten the consumers to be more informed and manufacturers to be more aware of the actual utilization of BEVs

Valuation of travel time for international long-distance travel - results from the Fehmarn Belt stated choice experiment

The geographical scope of travel varies from short distances in urban areas to long distances across cities and countries. While urban travel has been widely analysed in the literature, travel over longer distances and particularly across countries, has received much less attention. While this may be justified due to the number of travellers it cannot be justified when looking at the mileage consumption and its resulting environmental impacts. In this paper, we investigate international long-distance travel preferences related to travel between Scandinavia and Central Europe with particular focus on the Fehmarn Belt fixed link between Germany and Denmark to be opened in 2021. To facilitate long-term demand forecasts for the future fixed link, stated preference data were collected in 2011. Based on these data a discrete choice model for long-distance travellers was developed in order to estimate the value of travel time savings (VTTS). The final model, which was formulated as a nested logit model and included Box–Cox transformed travel time and cost attributes, revealed several interesting findings. Firstly, we found damping effects in both cost and time – most strongly in cost. Secondly, we found significant interactions among travel cost and time, and journey characteristics, such as distance and duration. This had direct impact on the VTTS, which was shown to decrease with distance and duration. Thirdly, we found that air travel implies a higher average VTTS, which is to be expected but rarely supported by empirical evidence